PERBANDINGAN METODE OPTIMASI SILHOUETTE, ELBOW, DAN GAP STATISTICS DALAM MENENTUKAN NILAI K TERBAIK PADA ANALISIS K-MEANS CLUSTERING

Abstract

Stunting is a condition of malnutrition status that is chronic in growth and development from the beginning of life, malnutrition puts children at greater risk of death. One of the efforts to overcome stunting is to determine in advance the provinces that need to be prioritized in handling the factors that cause stunting by grouping 34 provinces in Indonesia. This study uses k-means clustering to partition data according to their respective characteristics into the form of two or more clusters, determining the optimal number of clusters through elbow optimization methods, gap statistics and silhouette. The method used to test the best cluster results is the Davies Bouldin Index (DBI) method. The results of the elbow method clustering test produce K = 3 with a DBI value of 0.6392677, the gap statistics method produces K = 1 without DBI testing because only 1 cluster is formed, while the silhouette method produces K = 2 with a DBI value of 0.2116945. This shows that the results of clustering k-means with the silhouette method produce better cluster quality because it has a lower DBI value than other methods.